Methods, apparatus and computer program for generating and selecting number combination samples for use in a lottery from a pre-determined population of numbers

ABSTRACT

A system, computer program and method generate and select lotto number samples from a population to be played in a lottery game. The method uses the time between arrivals for samples having discrete characteristics as prime numbers, deficient even numbers, deficient odd numbers, perfect numbers abundant even numbers, maximum numbers, and string length of the sample. Once the sample sequence is created, the time between arrivals and the number of times the particular sequence occurs within a given time period can be estimated with the statistical Poisson and exponential distributions. Samples unlikely to occur are filtered out through a series of multi-function filtering methods. The remaining samples are the ones from which samples are selected and entered into the lotto drawing as a 6/44, 6/36, (5/44+1/44) or (5/54+1/42) type lotto games.

FIELD OF THE INVENTION

The invention relates generally to the field of lottery games, and in particular to methods, an apparatus and a computer program for selecting lottery number combinations to be entered in a lotto game. More particularly, the present invention relates to methods used to generate a sample that consist of discrete characteristics of the sample that makes up a sequence. The sample sequence is based on but not limited to prime, deficient even, deficient odd, abundant even, perfect, maximum numbers and sample string length. Samples with sequences that have characteristics unlikely to occur in a given time period are filtered through multi-function filtering methods using the statistical poison and exponential distributions. All or a portion of the remaining samples are selected and then entered into the lotto drawing for cash prizes.

BACKGROUND OF THE INVENTION

Many books have been written on the strategies of “How to play lottery games”. One of the most authoritative figures that have written numerous books on the subject of strategies is Gail Howard. In her book “Lottery Master Guide, 4^(th) Edition” she discusses wheeling systems, single digits, double digits and triple digits in strategies to select individual lottery numbers. The book contains an overwhelming amount of information that would be time consuming for lottery players to understand and to apply efficiently.

There also exist many patents that discuss the impact of state run lottery games in the United States. One can refer to U.S. Pat. Nos. 5,232,222, 5,330,185, 5,110,129, 4,813,676 and 4,583,736 to get an idea and a thorough understanding of lotteries impacts on state governments. These patents are herein incorporated b reference.

Currently, there is much material describing ways to generate individual random numbers from a set of numbers to play in lottery games. This material can be referenced to the use of birthdays, license plate numbers, age, hot numbers, cold numbers, electronic random number generators, dice, lucky numbers, quick pick machines and many more means for randomly generating individual numbers. For example, U.S. Pat. No. 5,157,602, uses a binomial distribution value method to select samples based on the sum of the individual numbers that make up the sample. They claim that by using the disclosed apparatus, the player is guaranteed at least one sample for a non-jackpot prize. The problem with this claim is it does not meet the lotto players need to win the whole jackpot.

Methods and apparatus focus on generating random numbers with no regard to a time period or the type of numbers that arrive during a certain time period. U.S. Pat. No. 6,371,482 “attempts to address this concern by comparing a generated random number with numbers from a previous drawn lottery and the occurrence of astronomical events. The disadvantage of this process is that no two lotteries are alike and the focus is again on individual numbers that make up the sample. Another draw back is that the player will have to update the astronomical tables used in the invention, which can be time consuming. As to date the applicant has not seen an apparatus on the market as a result of this patent.

Lotto type drawings are of two basic types and have jackpots based on odds of the player winning. The first type of drawing has no bonus ball associated with it. For example in a 6/54, 6/44 or 6/36 game there is no bonus ball. As an example of determining player odds in a pick 6 of 54 lotto game, the player has odds of 1:25,827,165 for winning the 1st prize known as the jackpot. Each time the jackpot is not won by anyone, it rolls over to another drawing at a higher prize amount. As the jackpot increases, more players purchase tickets to enter lottery numbers into the game. The more tickets entered, the more likely some one will win the jackpot. Lotto drawings in the example of a pick 6/54 game consist of using a machine that mixes fifty-four (54) balls that have numbers 1-54 imprinted on them to obtain randomness. A sample size of six (6) balls are drawn from the machine one at a time. After each ball is pulled the total number of balls are reduced by one and the mixing action is repeated until six (6) balls are drawn. The six (6) balls with the imprinted numbers make up the winning lottery numbers for the jackpot drawing. This selection method lottery's use is known as sampling a population without replacement. It is also known as a hyper-geometric sampling distribution.

The size of the lotto population is calculated by using combinational math taking six (6) of the fifty-four (54) numbers at a time, C.sup.54.sub.6. As mentioned, six (6) balls are drawn from a population of 54 balls. The six (6) balls also make up one sample from the lotto population. In the case of the pick (6/54) lotto game it is one sample out of a population size of 25,827,165. An example of a sample from a pick (6/54) lotto is 1-2-7-21-43-54 or 27-35-38-45-47-50. One can see the six (6) balls, which are represented by the numbers that make up the samples. One can also observe that for a non-bonus ball type lotto, the size of the numbers increase from left to right when placed in increasing order and no number repeats. The probability of a player winning the jackpot prize can be calculated using the odds and the population size. From the odds the probability can be calculated to be 1/25,827,165 or 0.000000039 or 3.9×10−8. The one (1) in the numerator represents the sample size of six (6) balls and the 25,827,165 in the denominator represent the lotto population. Probability is a number given between 0.0 and 1.0. From the small size of the probability for a player winning the 1 st prize in a pick 6/54 lottery, one can understand the difficult time a player would have in generating and selecting potential winning samples.

The second type of lotto has a bonus ball as example (5/44+1/44) or (5/54+1/42) the second term after the plus(+) sign represents the bonus ball. This type of lotto consists of two drawings combined into one. In the first half of the drawing 5 of 44 numbers are selected and in the second half of the drawing one bonus ball is selected from a total of 44 balls. For example a pick (5/44+1/44) lotto game has a player's odds of 1:47,784,352 for winning the 1st prize known as the jackpot. As in the non-bonus ball type lotto, each time the jackpot is not won by anyone, it rolls over to another drawing at a higher prize amount. As the jackpot increases, more players purchase tickets to enter lottery numbers into the game.

The five (5) in the term 5/44 represents the required numbers needed to make up the left part of the sample from the lotto population. Lotto drawings in the example of a pick 5/44+1/44 game consist of using two machines that mixes forty-four (44) balls in each machine that have numbers 1-44 imprinted on them to obtain randomness. A sample size of five (5) balls are drawn from the first machine one at a time. After each ball is pulled from the first machine, the total number of balls are reduced by one and the mixing action is repeated until five (5) balls are drawn. A sample size of one ball is drawing from the second machine. The six (6) balls, five (5) from the first machine and one from the second machine, with the imprinted numbers make up the winning lottery numbers for the jackpot drawing. The five (5) balls are drawn from a population of 44 balls and 1 ball, the bonus ball, is drawing from a population of 44 balls. The six(6) balls also make up one sample from the lotto population. In the case of the pick (5/44+1/44) lotto game it is one sample out of a population size of 47,784,352. An example of a sample from a pick (5/44+1/44) lotto is 1-2-7-21-43-14 or 27-35-38-45-47-5. A lotto drawing with a bonus ball number present increases from left to right for positions one through five (5) in the sample. The 6^(th) position number can be less than, equal to or greater than any of the numbers in positions 1 through 5. The size of the lotto population is calculated by using combinatorial math taking five (5) of the forty-four (44) numbers at a time, C.sup.44.sub.5 and multiplying by the 44 numbers that represent the bonus ball. The probability of a player winning the jackpot prize can be calculated using the odds and the population size. From the odds the probability can be calculated to be 1/47,784,352 or 0.000000021 or 2.1×10−8. The one (1) in the numerator represents the sample size of six (6) balls and 47,784,352 in the denominator represent the lotto population. As with the non-bonus ball game, the small size of the probability for a player winning the first prize in a pick 5/44+1/44 lottery, one can understand the difficult time a player would have in generating and selecting potential winning samples with a bonus ball.

In review of the current technologies, there exist no invention or patent that addresses the needs of lottery players to automatically select samples from a population based on the sample's discrete characteristics as prime number, deficient even number, deficient odd number, abundant even number, perfect number, maximum number and string length that has an arrival time and expected number of occurrences during a certain time period.

SUMMARY OF THE INVENTION

The present invention generates sample lottery numbers to be used when playing in a lottery game of chance. This invention selects samples that are calculated to likely occur within a given time period. These calculations use a multi-function filtering technique based on but not limited to sample discrete characteristics as prime numbers, deficient even numbers, deficient odd numbers, abundant even numbers, perfect numbers, maximum numbers and string length of the sample. This invention reduces the guesswork of the player and make the generation and selection process of samples from the lotto population to be entered into lottery games more efficient for the player. The invention also makes the selection process for choosing samples to be entered into the lotto game more efficient and less emotional. This invention also incorporates an apparatus to run a computer program to print to a printer and store the samples on a hard drive using a computer consisting of a processor, memory, software, hard drive, compact disc writer.

The present invention comprises a method, apparatus and computer program used to generate and select samples from a lotto population to be entered in to a lottery game of chance. The methods use a multi-function filtering technique that takes the form of computer code to reject samples with sequences unlikely to occur in a specified time period. Sequences for the filtering functions are created using discrete sample characteristics as prime number, deficient even number, deficient odd number, abundant even number, perfect number, maximum number and string length for the sample. The sample sequence consists of 0's and 1's, which indicate if the sequence characteristic are present or not present in the sample. Computer code consisting of the statistical poison and exponential distributions, is run to find the time between arrivals of sample sequences and the number of times the sequence occur in a specific time period can be estimated. The arrival time and number of occurrences are then checked to see if they are within the limits for the particular sequence. If the values are, then the sequence is accepted which makes the sample acceptable. If the sequence fails the test, then the sample is filtered out (sample rejected) and stored in rejected databases on the hard drive. The samples passing through the filters are stored on the hard drive and printed out on paper using a device as a computer, hard drive, compact disc writer and printer.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of the system components used to run the computer code in order to generate samples to be entered into the lotto game. These components can include a computer, keyboard, mouse (pointing device), display, hard drive, compact disc writer, printer.

FIG. 2 is a flow diagram of the general method of the present invention.

FIG. 3 is a detailed flow diagram of the multi-filtering process used to generate samples to be entered into a lotto game.

FIG. 4 is a diagram illustrating the statistical poison and exponential distributions.

FIG. 5 is a table showing the distribution of samples for a 6/54 lotto game.

FIG. 6 is an exponential graph of the distribution of the samples in the I-Column of a 6/54 lotto game.

FIG. 7 is a lognormal graph of the distribution of the samples in the J-Column of a 6/54 lotto game.

FIG. 8 is a normal graph of the distribution of the samples in the K-Column of a 6/54 lotto game.

FIG. 9 is a normal graph of the distribution of the samples in the L-Column of a 6/54 lotto game.

FIG. 11 is a lognormal graph of the distribution of the samples in the M-Column of a 6/54 lotto game.

FIG. 12 is an exponential graph of the distribution of the samples in the N-Column of a 6/54 lotto game.

FIG. 12 is a table showing the regions number fall into to determine the discrete sample characteristics for samples in a 6/54 lotto game.

FIG. 13 is a table showing the distribution of samples for a 5/44+1/44 lotto game.

FIG. 14 is an exponential graph of the distribution of the samples in the I-Column of a 5/44+1/44 lotto game.

FIG. 15 is a lognormal graph of the distribution of the samples in the J-Column of a 5/44+1/44 lotto game.

FIG. 16 is a normal graph of the distribution of the samples in the K-Column of a 5/44+1/44 lotto game.

FIG. 17 is a log-normal graph of the distribution of the samples in the L-Column of a 5/44+1/44 lotto game.

FIG. 18 is an exponential graph of the distribution of the samples in the M-Column of a 5/44+1/44 lotto game.

FIG. 19 is a binomial graph of the distribution of the samples in the N-Column of a 5/44+1/44 lotto game.

FIG. 20 is a table showing the regions number fall into to determine the discrete sample characteristics for samples in a 5/44+1/44 lotto game.

DEFINITION OF TERMS

The following definition of terms are provided to help with clarity for understanding the present and novel invention:

Winning Sample Database—Samples composed of a set of individual numbers drawn from a lottery by the state lottery authority or designated agent and stored on the hard drive and compact disc storage media.

Rejected Sample Database—Samples rejected from the lotto population that are stored on the hard drive and compact disc storage media.

Selected Sample Database—Samples selected from a population by the present invention to be entered into a lottery by the player for cash and prizes and stored on the hard drive and compact disc storage media.

Element—The individual numbers that make up a sample.

Sample—A set of individual numbers taken from a population of numbers.

Discrete characteristics—A property of an element, a number or sample such as prime number, deficient even number, deficient odd number, abundant even number, perfect number, string length or sum of sample elements.

Sequence—A set of discrete characteristics that are properties of a sample.

Filter function—Code in a computer program that gives instructions to accept or reject a sample whose sequence of discrete characteristics does not meet requirements.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, shown is a detail configuration of the system 10 to generate samples from a lottery population to be entered into a lottery game. The system comprises of the player/user 12, computer 14, (which is comprised of a case 16, central processing unit (CPU) 18, memory 20 and an operating system (OS) 22, liquid crystal display (LCD) 24, keyboard 26, mouse 28, hard drive 30, CD writer 32, CD storage media 34, printer 36, paper for reporting 38 visual images and lottery entry form 40. The player/user 12 uses the keyboard 26 and the mouse 28 to enter commands to start the multi-filtering software computer program 52. Commands are input in the form of “yes/no” responses to questions prompted by the multi-filtering software computer program 52 and are viewed by the player/user 12 on the LCD display 24. A display of an analog monitor type can also be used to show the information being input by the player/user 12 with the keyboard 26 or mouse 28. The multi-filtering software 52 computer program is stored on the hard drive 30 located inside the computer. The multi-filtering software computer program 52 is loaded into the computer memory 20 where it is run by the computer central processing unit (CPU) 18. The user is prompted to enter any previous winning samples from lottery drawings. Data in the form of the previous winning lotto samples 42 (10-12-23-37-41-28) are entered by the player/user 12 by means of the keyboard 26 and mouse 28. The player/user 12 can view the data that is being entered by the keyboard 26 and mouse 28 on LCD display 24. The data of previous winning lotto samples 42 are stored to a past winning samples file 44 located on the hard drive 30 inside the computer 14. These samples are also stored on a CD storage media 32 located inside the CD writer 34. Many companies such as Hewlett Packard, Sony and Plextor manufacture CD writers and storage media. The multi-filtering software 52 computer program writes a report of the lottery samples to be entered onto the lottery entry form 40 to a sample selected file 46 stored on the hard drive 30 located inside the computer 14. A paper report 38 of the visual image of the file also known as a hardcopy of the lotto samples selected is then printed using a printer 36 of inkjet or laser type.

The LCD display 24, keyboard 26, mouse 28 and printer 36 are connected to the rear of the computer 14 using cables to transmit data to each device. The cables are not shown on the figure for the sake of maintaining clear illustration of the apparatus. The preferred embodiment has the CPU 18, memory 20, hard drive 30, CD writer 32 located inside the computer 14. While this is the preferred embodiment, those skilled in the arts may desire devices to be located external to the computer without violating the claims.

FIG. 2 is a flow diagram that illustrates the general method of the present invention. In this general approach step 200 generates a lottery sample such as 08-09-14-31-45-48. Step 202 determines the string length of the lottery sample. This string length is equal to the total number of digits in the string. For example the sample 8-9-14-31-45-48 has a string length of ten (10) characters. The next step 204 performs an analysis of the string to determine if the string meets a set of defined lottery sample characteristics. If the determination in step 206 is that the sample does not contain the desired characteristics, the sample is rejected. Steps 204 and 206 can be performed in an iterative process for each considered characteristic. With the iterative approach, once the sample does not meet a particular characteristic, the process rejects the sample. If in step 206, the sample does determine the defined characteristics, the process moves to step 208, where there is a determination of whether the sample already exists in the database. This database contains a list of all the past winning lottery samples. If the sample does already exist in the database, the sample is rejected. If the sample does not exist in the database, then in step 210, the sample is added to the database of potential lottery samples to be used in a drawing. Referring back to steps 206 and 208, lottery samples that do not contain the desired characteristics or samples that are already in the database are rejected in step 212. Whether a sample is added to the database or rejected, the process moves to step 214, which makes a determination of whether to get another lottery sample or to end the process. If the determination is to get another lottery sample, the process returns to step 200 and repeats the process. If the determination is not to generate another lottery sample, the process ends.

Referring to FIG. 4, shown is a diagram illustrating the Poisson statistical arrival process. The dot symbol 120 represents samples with the described sample characteristics in the methods that follow that are due to arrive in a given interval of time. As shown in the time interval, T=1, there are five (5) dots (X1, X2, X3, X4, X5) which represents at least five (5) samples with the desired characteristics that are predicted to arrive in the interval of time, T=1. The Poisson statistical distribution is known in the field of statistics as a discrete distribution that can be used to determined the number of events that will likely take place in a given interval of time. In the present invention the Poisson distribution is used to measure the number of samples with the same characteristics drawn from a lottery population that will occur in a given number of drawings. Sample characteristics are created using the described methods that follow.

The statistical exponential distribution is known in the field of statistics as a continuous distribution that measures the amount of time between the occurrences of events. In the present invention the exponential distribution is used to measure the number of drawings that will occur between two samples with the same characteristics drawn from a lottery population. Again referring to FIG. 4., in the time interval, T=2 are three dots (X1, X2 and X3) that occur at times t6, t7 and t8. The times t6, t7 and t8 represent the time between lottery drawings. This difference is also known as the time between arrivals or the time between trials.

Referring now to FIG. 3, the methods used by the multi-filtering software 52 will now be disclosed. The multi-filtering software 52 comprises of ten (10) methods that are used to generate sequences for sample characteristics and then uses the Poisson and exponential statistical distributions to check if the samples from the lottery population are due to arrive in the next drawing. The ten methods include: 1) sample string length method, 2) prime number method, repeat elements in sample method, 3) sample sum method, 4) sample KLM sum method, 5) sample KLM sum method, 6) sample I-column element method, 7) sample probability method 8) skewed method 9) skewed method for 5/55+1/44 lotto game and 10) sample table regions methods. The ten (10) methods used to generate the sequences for sample characteristics will now be disclosed in detail.

Sample String Length Method

The sample string length consists of the number of characters that make up the sample drawn from the lottery population. For example the sample 08-09-14-31-45-48 has a string length of ten (10) characters. The zeros (0's) are not counted. The sample string length allows for the samples in the lottery population to be subdivided into smaller groups. The smaller groups are then converted to a sequence of 6,7,8,9,10,11 or 12 to represent the discrete characteristic of the sample string length. Most lotteries have samples with string lengths between six (6) and twelve (12) characters. For lotteries that have a bonus ball the sample string length is further divided into a type 1 and a type 2. For example a sample 8-10-12-32-45-7 has a string length ten (10) characters. Because the sample has a bonus ball of seven (7), a single character, it is considered a sample of string length ten (10) and type 1 (written as 10_(—)1). The bonus ball will determine if the sample is a type 1. Samples with bonus balls between one and nine (9) are type 1 samples. A sample 8-9-12-32-45-31 also has a string length of ten (10) characters and is considered a sample of string length ten (10) and type 2 (written as 10_(—)2). Using computer code for the statistical Poisson and exponential distribution the samples of a particular string length sequence arriving in the next drawing can be determined. For those skilled in the arts, they will recognize this process as a time between arrivals.

Prime, Deficient Even, Deficient Odd, Abundant Even, Perfect, Unity and Maximum Method

Prime, deficient even, deficient odd, abundant even, perfect, unity and maximum type numbers are well known in the field of mathematics. The definition of a prime number is a number divisible by one and itself. The numbers 2, 3, 5, 7, 11, 13, 17, 23, 29, 31, 37, 41, 43, 47 and 53 are examples of prime numbers. The definition of a deficient even number is a number whose factors when added together are less than the individual number. The numbers 4, 8, 10, 14, 16, 22, 26, 34 and 38 are examples of deficient even numbers. The definition of a deficient odd number is a number whose factors when added together are less than the individual number. The numbers 9, 15, 21, 25, 27, 33, 35, 39 and 45 are examples of deficient odd numbers. The definition of an abundant even number is a number whose factors when added together are greater than the individual number. The numbers 12, 18, 24, 36, 42 and 48 are examples of abundant even numbers. The definition of a perfect number is a number whose factors when added together are equal to the individual number. The numbers 6 and 28 are examples of perfect numbers. The definition of a maximum number is a number that appears the greatest number of times in the samples position in the population. The numbers 11, 22, 33 and 44 are examples of maximum numbers. Samples from a lottery population consist of these types of numbers. The numbers are used to characterize samples drawn from a lottery population. A sequence of the sample characteristics is generated using zero's (0) and one's (1) to indicate the non-presence or presence of the type of number in the sample. Using code for the Poisson and exponential statistical distributions, it can then be determined if a sample with the particular sequence characteristic is likely to occur in the next drawing.

Repeat Elements in Samples Method

On occasions a player may observe lottery numbers repeating from drawing to drawing for samples. The repeating and non-repeating of numbers in samples is converted to a sequence of zero's (no repeat) and one's (repeat). Applying code for the Poisson and exponential statistical distributions, it can then be determined if a sample with the particular sequence characteristic is likely to occur in the next drawing.

Sample Sum Method

The sample characteristic “Sample Sum” is comprised of the total numerical sum of the individual numbers that make up the sample. For example in the sample 08-09-14-31-45-48, the sample sum is 155. Using code for the normal distribution the sample sum is converted into a sequence by determining which region of the distribution the sum falls. The distribution is divided into a region A, region B, region C and region D. A sequence of zero's (0's) and one's (1's) is used to develop the sample characteristic by checking the non-presence or presence of the sum in a particular region. Using code for the Poisson and exponential statistical distributions, the sample sequence can be determined if it will arrive for the next drawing.

Sample KLMSum method

The sample characteristic “KLMsum” is comprised of the sum of the middle three numbers in the sample. In the example of a lottery sample that has six (6) numbers as 08-09-14-31-45-48 the KLM sum is the sum of the numbers 14-31-45 which is ninety (90). Using the normal statistical distribution the sample sum is converted to a sequence of zero's and one's (1's) based on which region KLMsum is not present or present in. The normal distribution can be divided into region A, region B, region C, region D. Using code from the Poisson and exponential statistical distributions, a sample arriving in the next drawing with the particular sequence can be determined.

Sample I-Column Element Method

The sample characteristic I-Column element is comprised of the number in the first position of the sample. For example in lottery sample 08-09-14-31-45-48; the I-column element is the number eight (8). A sequence for the sample having this characteristic is generated using zero's(0's) and ones(1's) to indicate the non-presence or presence of the characteristic in the sample. Code for the statistical Poisson and exponential distribution is used to check if the sample with the particular sequence is likely to occur in the next drawing.

Sample Probability method

The sample characteristic “sample probability” is comprised of the sum of the total number of occurrences of the numbers in the positions in sample divided by the lotto population size. The number of occurrences for a number in a sample is calculated by taking the number of times the number in the first position occurs in that position added to the number of times the second number occurs in the second position added to the number of times the third number in the sample occurs in the third position added to the number of times the fourth number in the sample occurs in the fourth position added to the number of times the fifth number occurs in the fifth position added to the number of times the sixth number occurs in the sixth position.

A probability interval is developed by dividing the probabilities from 0.0000-1.000. A sequence for the sample is developed using zero's(0's) and one's(1's) to check the non-presence or presence of the probability in a particular interval. Using code for the statistical Poisson and exponential distribution, a sample with this sequence can be determined if it will appear in the next drawing.

Skewed Method

Referring to FIG. 5, 5 a and 5 b is a table consisting of six(6) columns I labeled thru N that shows the total occurrences of the numbers that make up the population samples for a 6/54 lotto game and the probabilities. The six(6) columns are further sub-divided into five (5) columns each to show the total occurrences of samples, cumulative occurrences of samples, the probability and cumulative probability of samples with the numbers 1 thru 40 in the population. The I-column of the table in FIG. 5 represents numbers in the first position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 8 appears in the I-column a total of 1,370,754 times in the population.

The J-column shows the total occurrences of the numbers 2 thru 41 in the population. The J-column in the table of FIG. 5 represents numbers in the second(2^(nd)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 9 appears in the J-column a total of 1,191,960 times in the population.

The K-column shows the total occurrences of the numbers 3 thru 42 in the population. The K-column in the table of FIG. 5 a represents numbers in the third(3^(rd)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 14 appears in the K-column a total of 770,640 times in the population.

The L-column shows the total occurrences of numbers 4 thru 43 in the population. The L-column in the table of FIG. 5 a represents numbers in the fourth(4^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 38 appears in the L-column a total of 932,400 times in the population.

The M-column shows the total occurrences of numbers 5 thru 41 in the population. The M-column in the table of FIG. 5 b represents numbers in the fifth(5^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 41 appears in the M-column a total of 1,188,070 times in the population.

The N-column shows the total occurrences of numbers 1 thru 44 in the population. The N-column in the table of FIG. 5 b represents numbers in the sixth(6^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 44 appears in the N-column a total of 962,598 times in the population.

Referring now to FIGS. 6, 7, 8, 9, 10 and 11 are six statistical distributions illustrating the dispersion of the numbers in the table of FIGS. 5, 5 a and 5 b that make up the population of samples for a 6/44 lotto game. These six distributions can be illustrated for any particular lottery game of the lotto type 6/44, 6/54, 6/36, (5/52+1/44) etc. Referring to FIG. 6 shown is a statistical exponential distribution that shows how the numbers 1 thru 49 are distributed in the I-column of the table in FIG. 5. The distribution has a right-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 7 is a statistical log-normal distribution that shows how the numbers 2 thru 50 are distributed in the J-column of the table in FIG. 5. The distribution has a right-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 8 is a statistical normal distribution that shows how the numbers 3 thru 51 are distributed in the K-column of the table in FIG. 5 a. The distribution is symmetrical about a middle value called the average or mean and falls off at the right and left ends. The fall off at the ends suggests samples with numbers in these regions rarely occur.

FIG. 9 is a statistical normal distribution that shows how the numbers 4 thru 52 are distributed in the K-column of the table in FIG. 5 a. The distribution is symmetrical about a middle value called the average or mean and falls off at the right and left ends. The fall off at the ends suggests samples with numbers in these regions rarely occur.

FIG. 10 is a statistical log-normal distribution that shows how the numbers 5 thru 53 are distributed in the M-column of the table in FIG. 5 b. The distribution has a left-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 11 is a statistical exponential distribution that shows how the numbers 6 thru 54 are distributed in N-column of the table in FIG. 5 b. The distribution has a left-hand-tail, which suggests samples with numbers in the tail region rarely occur.

Skewed Method for (5/55+1/44) Lotto Game

FIGS. 13, 13 a and 13 b are tables consisting of six(6) columns I labeled thru N that shows the total occurrences of the numbers that make up the population samples for a (5/54+1/44) lotto game and the probabilities. The six(6) columns are further sub-divided into five (5) columns each to show the total occurrences of samples, cumulative occurrences of samples, the probability and cumulative probability of samples with the numbers 1 thru 40 in the population. The I-column in FIG. 5 of the table represents numbers in the first position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 8 appears in the I-column a total of 2,591,820 times in the population.

The J-column shows the total occurrences of the numbers 2 thru 41 in the population. The J-column in of FIG. 5 of the table represents numbers in the second (2^(nd)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 9 appears in a total of J-column 2,303,840 times in the population.

The K-column shows the total occurrences of the numbers 3 thru 42 in the population. The K-column in FIG. 5 a of the table represents numbers in the third (3^(rd)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 14 appears in the total of K-column 1,492,920 times in the population.

The L-column shows the total occurrences of numbers 4 thru 43 in the population. The L-column in FIG. 5 a of the table represents numbers in the fourth (4^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 38 appears in the total of L-column 2,051,280 times in the population.

The M-column shows the total occurrences of numbers 5 thru 41 in the population. The M-column in FIG. 5 b of the table represents numbers in the fifth(5^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 41 appears in the M-column a total of 4,021,160 times in the population.

The N-column shows the total occurrences of numbers 1 thru 44 in the population. The N-column in FIG. 5 b of the table represents numbers in the sixth(6^(th)) position of the lottery sample. For example in the lottery sample 08-09-14-38-41-44 the number 44 appears in the N-column a total of 1,086,280 times in the population.

Referring now to FIGS. 14, 15, 16, 17, 18 and 19, shown are six statistical distributions illustrating the dispersion of the numbers in the table of FIGS. 12, 12 a and 12 b that make up the population of samples for a (5/44+1/44) lotto game. These six distributions can be illustrated for any particular lottery game of the lotto type 6/44, 6/54, 6/36, (5/52+1/44) etc. FIG. 14 is a statistical exponential distribution that shows how the numbers 1 thru 40 are distributed in the I-column of the table in FIG. 13. The distribution has a right-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 15 is a statistical log-normal distribution that shows how the numbers 2 thru 41 are distributed in the J-column of the table in FIG. 13. The distribution has a right-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 16 is a statistical normal distribution that shows how the numbers 3 thru 42 are distributed in the K-column of the table in FIG. 13 a. The distribution is symmetrical about a middle value called the average or mean and falls off at the right and left ends. The fall off at the ends suggests samples with numbers in these regions rarely occur.

FIG. 17 is a statistical log-normal distribution that shows how the numbers 4 thru 43 are distributed in the L-column of the table in FIG. 13 a. The distribution has a left-hand-tail, which suggests samples with numbers in the tail region rarely occur.

FIG. 18 is a statistical exponential distribution that shows how the numbers 5 thru 44 are distributed in M-column of the table in FIG. 13 b. The distribution has a left-hand-tail, which suggest samples with numbers in the tail region rarely occur.

FIG. 19 is the statistical binomial distribution that shows how the numbers 1 thru 44 are distributed in the N-column of the table in FIG. 13 b. The distribution is essentially a flat line, which suggests each number in the sample is equally likely to occur.

Sample Table Regions Method

Referring now to the table in FIG. 12 for a 6/54 lotto, there are six(6) columns in the table labeled column I, column J, column K, column L, column M, and column N. In column I are shown the numbers 1 thru 49 that make up the first positions of the sample from the 6/54 lotto population. The columns are partitioned into four regions. Region 1 in column I comprises of numbers 1 thru 12, region 2 comprise of numbers 13 thru 24, region 3 comprise of numbers 25 thru 36 and Region 4 comprise of numbers 37 thru 49.

Region 1 in column J comprises of numbers 7 thru 18, region 2 comprise of numbers 3 thru 6 and 19 thru 26, region 3 comprise of numbers 28 thru 42 and Region 4 comprise of numbers 43 thru 54. Region 1 in column K comprises of numbers 17 thru 28, region 2 comprise of numbers 11 thru 16 and 29 thru 34, region 3 comprise of numbers 7 thru 10 and 35 thru 42 and Region 4 comprise of numbers 3 thru 6 and 43 thru 51. Region 1 in column L comprises of numbers 27 thru 38, region 2 comprise of numbers (21 thru 26) and (29 thru 44), region 3 comprise of numbers (13 thru 20) and (45 thru 48) and Region 4 comprise of numbers (4 thru 12) and (49 thru 52). Region 1 in column M comprises of numbers 37 thru 48, region 2 comprise of numbers 29 thru 36 and 49 thru 52, region 3 comprise of numbers 18 thru 28,53 and Region 4 comprise of numbers 5 thru 17. Region 1 in column N comprises of numbers 6 thru 17, region 2 comprise of numbers 18 thru 29, region 3 comprise of numbers 30 thru 41 and Region 4 comprise of numbers 42 thru 54.

A sequence for the sample generated from the population is created by assigning the value of 1 thru four(4) for the region the number in the sample appears. For example the sample 08-09-14-38-41-44 would form the sequence 1-1-2-1-1-4. The sequence represents the region the numbers the sample contains are from. The Poisson and exponential distributions would then be applied to this sequence to determine if the sample contained the characteristics due to arrive for the next drawing.

Referring now to the table in FIG. 20 for a (5/44+1/44) lotto, there are six(6) columns in the table labeled column I, column J, column K, column L, column M, and column N. In column I are shown the numbers 1 thru 40 that make up the first positions of the sample from the lotto (5/44+1/44) population. The columns are partitioned into four regions. Region 1 in column I comprise of numbers 1 thru 10, region 2 comprise of numbers 11 thru 20, region 3 comprise of numbers 21 thru 30 and Region 4 comprise of numbers 31 thru 40.

Region 1 in column J comprises of numbers 8 thru 17, region 2 comprise of numbers 4 thru 7 and 18 thru 23, region 3 comprise of numbers 2,3, and 24 thru 31 and Region 4 comprise of numbers 32 thru 41. Region 1 in column K comprises of numbers 18 thru 27, region 2 comprise of numbers 13 thru 17 and 28 thru 32, region 3 comprise of numbers 8 thru 12 and 33 thru 37 and Region 4 comprise of numbers 3 thru 7 and 38 thru 42. Region 1 in column L comprises of numbers 28 thru 37, region 2 comprise of numbers 22 thru 27 and 37 thru 41, region 3 comprise of numbers 14 thru 21 and 42 thru 43 and Region 4 comprise of numbers 4 thru 13. Region 1 in column M comprises of numbers 5 thru 14, region 2 comprise of numbers 15 thru 24, region 3 comprise of numbers 25 thru 34 and Region 4 comprise of numbers 35 thru 44. Region 1 in column N comprises of numbers 1 thru 11, region 2 comprise of numbers 12 thru 22, region 3 comprises of numbers 23 thru 33 and Region 4 comprise of numbers 34 thru 44.

A sequence for the sample generated from the population is created by assigning the value of 1 thru four(4) for the region the number in the sample appears. For example the sample 08-09-14-38-41-44 would form the sequence 1-1-2-2-4-4. The sequence represents the region the numbers the sample contains are from. The Poisson and exponential distributions would then be applied to this sequence to determine if the sample contained the characteristics due to arrive for the next drawing.

The present invention will now be summarized in its entirety by referring to FIG. 3. The computer program begins execution at start 48 and proceeds to step 50 to execute multi-filtering software 52 algorithm to generate a sample from the lottery population. Once the sample is generated, execution then proceeds to step 54 to find sample characteristic “string length”. At step 56 the multi-filtering software 52 checks to see if the sample contain the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 60. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 58 where multi-filtering software 52 algorithm stores the sample into the rejected sample string length database. A backward-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 60 multi-filtering software 52 algorithm finds the sample characteristics “I column element MTBA”. At step 62 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 66. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 64 where multi-filtering software 52 algorithm stores the sample into the rejected sample I column element database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 66 multi-filtering software 52 algorithm finds the sample characteristics “prime-defe-defo-abue-maxc-perf”. At step 68 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 72. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 70 where multi-filtering software 52 algorithm stores the sample into the rejected sample prime-defe-defo-abue-maxc-perf database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 72 multi-filtering software 52 algorithm finds the sample characteristics “KLMSUM”. At step 74 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 78. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 76 where multi-filtering software 52 algorithm stores the sample into the rejected sample KLMSUM database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 78 multi-filtering software 52 algorithm finds the sample characteristics “Skewed”. At step 80 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 84. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 82 where multi-filtering software 52 algorithm stores the sample into the rejected sample “Skewed” database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 84 multi-filtering software 52 algorithm finds the sample characteristics “Sample Sum Seq Active”. At step 86 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 90. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 88 where multi-filtering software 52 algorithm stores the sample into the rejected sample “Sample Sum Seq Active” database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 90 multi-filtering software 52 algorithm finds the sample characteristics “Repeat elements in sample”. At step 92 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 96. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 94 where multi-filtering software 52 algorithm stores the sample into the rejected sample “Repeat elements in sample” database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 96 multi-filtering software 52 algorithm finds the sample characteristics “Sample table regions”. At step 98 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 102. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 100 where multi-filtering software 52 algorithm stores the sample into the rejected sample “Sample table regions” database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 102 multi-filtering software 52 algorithm finds the sample characteristics “Sample probability”. At step 104 multi-filtering software 52 checks to see if the sample contains the desired characteristics. Should the sample contain the desired characteristics, execution proceeds step along the “YES” path to step 106. Should the sample not contain the desired characteristics, then execution proceeds step along the “NO” path to step 108 where multi-filtering software 52 algorithm stores the sample into the rejected sample “Sample probability” database. A backwards-step to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 108 multi-filtering software 52 algorithm checks the sample generated from the population against samples stored in the past winning sample database 44. At step 110 multi-filtering software 52 checks to see if the sample exists in the past winning sample database 44. Should the sample not exist in the database, execution proceeds along the “NO” path to step 112. Should the sample exist in the past winning sample database 44, then a backwards-step along the “YES” path to step 50 is then made to execute the multi-filtering software 52 to generate the next sample from the lottery population.

In Step 112 multi-filtering software 52 algorithm stores the generated lottery population sample in a file located on the hard drive 30. Execution then proceeds to step 114. In Step 114 multi-filtering software 52 algorithm sends the generated lottery population sample to printer device 36 to be printed on report paper 38. Execution then proceeds to step 116. In Step 116 multi-filtering software 52 algorithm checks to see if the last sample in the lottery population has been generated. If the last sample has not been generated from the lottery population then a backwards-step is made to step 50 where the multi-filtering software 52 generates the next sample from the lottery population. If the last sample has been generated from the lottery population, then execution proceeds to step 118. At step 118 multi-filtering software 50 algorithm terminates the program.

Those that have a skill and understanding of the prior arts will have a great appreciation for the embodiment of the current invention which will give the lottery player a competitive advantage when selection samples to play in a lottery game. Although the embodiment of the current invention was explained in much detail, any changes modifications or variations are within the spirit and scope of the attached claims. 

1. A method for generating a population of number combination samples and selecting number combination samples for use in a lottery from the generated population of number combinations comprising the steps of: generating a lottery sample string comprising a plurality of numbers; determining a string length for the generated sample lottery string; defining a set of lottery sample characteristics; determining whether the generated lottery sample string contains the defined set of lottery sample characteristics; when the generated lottery sample does contain the defined lottery characteristics, determining whether the lottery sample string already exist in a database containing previously winning lottery number combinations; and adding the generated lottery sample string to the database when the determination is that the generated combination does not already exist in the database.
 2. The method as described in claim further comprising before said generating a lottery sample string step, the step of creating a database containing previously winning lottery number combinations and potentially winning lottery sample strings which are not previously winning samples.
 3. The method as described in claim 2 further comprising after said adding the generated lottery combination to the database step, the step of selecting a lottery number string from the potentially winning lottery sample strings stored in the database.
 4. The method as described in claim 1 wherein said determining lottery sample characteristics step further comprises multiple filtering functions that are performed on the generated lottery sample string.
 5. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can comprise a string length function.
 6. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can comprise a sample skewed distribution for sample function.
 7. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can comprise Sample Sum Sequence Active function.
 8. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can a repeat elements in Sample function.
 9. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can a sample table regions function.
 10. The method as described in claim 4 wherein said multiple filtering functions that are performed on a generated lottery sample string can a sample probability function.
 11. The method as described in claim 2, wherein said determining whether the lottery sample string already exist in a database containing previously winning lottery number combinations step further comprises the step of comparing a generated sample with previous winning samples located in the database.
 12. The method as described in claim 1 wherein said determining whether the generated lottery sample string contains the defined set of lottery sample characteristics step further comprises using a statistical Poisson distribution to determine number of occurrences of sample sequences in a period of time.
 13. The method as described in claim 1 wherein said determining whether the generated lottery sample string contains the defined set of lottery sample characteristics comprises using statistical exponential distribution to determine the time between arrivals of samples discrete characteristics sequences.
 14. A system for generating a population of number combination samples and selecting number combination samples for use in a lottery from the generated population of number combinations comprising: a computing device having a microprocessor, memory, hard drive, CD writer, CD storage media, keyboard, mouse, Liquid crystal display(LCD) and printer used to select and print lotto numbers to be entered by a player into a lotto game of chance for cash and prizes; a random access memory that holds computer program code executed by the microprocessor; and a multiple-filtering software program that executes on said computing device, said software program capable of generating a lottery sample string comprising a plurality of numbers, determining a string length for the generated sample lottery string, determining whether the generated lottery sample string contains the defined set of lottery sample characteristics and determining whether the lottery sample string already exist in a database containing previously winning lottery number combinations.
 15. The system as described in claim 14 further comprising of an output display used to view information generated by said multiple-filtering software program.
 16. The system as described in claim 14 further comprising of a printer and media used to capture a hard copy of data generated by said multiple-filtering software program.
 17. A computer program product in a computer readable storage medium to for generating a population of number combination samples and selecting number combination samples for use in a lottery from the generated population of number combinations comprising: instructions for generating a lottery sample string comprising a plurality of numbers; instructions for determining a string length for the generated sample lottery string; instructions for defining a set of lottery sample characteristics; instructions for determining whether the generated lottery sample string contains the defined set of lottery sample characteristics; when the generated lottery sample does contain the defined lottery characteristics, instructions for determining whether the lottery sample string already exist in a database containing previously winning lottery number combinations; and instructions for adding the generated lottery sample string to the database when the determination is that the generated combination does not already exist in the database.
 18. The computer program product as described in claim 17 further comprising before said generating a lottery sample string instructions, instructions for creating a database containing previously winning lottery number combinations and potentially winning lottery sample strings which are not previously winning samples.
 19. The computer program product as described in claim 18 further comprising after said adding the generated lottery combination to the database instructions, instructions for selecting a lottery number string from the potentially winning lottery sample strings stored in the database.
 20. The computer program product as described in claim 17 wherein said determining lottery sample characteristics instructions further comprise instructions for multiple filtering functions that are performed on the generated lottery sample string. 